A Career in Data Is Not Always a Straight Line, and That’s Okay
A Career in Data Is Not Always a Straight Line, and That’s Okay
数据职业生涯并非总是一条直线，这没关系
Author Spotlights: A Career in Data Is Not Always a Straight Line, and That’s Okay 作者聚焦：数据职业生涯并非总是一条直线，这没关系
Sabrine Bendimerad on why flexibility is a crucial data science skill, the risks of outsourcing human thinking to AI agents, and the changing terrain of career paths today. Sabrine Bendimerad 探讨了为何灵活性是数据科学的关键技能、将人类思维外包给 AI 智能体的风险，以及当今职业道路不断变化的格局。
In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science and AI, their writing, and their sources of inspiration. Today, we’re thrilled to share our conversation with Sabrine Bendimerad. 在“作者聚焦”系列中，TDS 编辑们与社区成员探讨了他们在数据科学和人工智能领域的职业道路、写作心得以及灵感来源。今天，我们很高兴分享与 Sabrine Bendimerad 的对话。
Sabrine is an applied math engineer who has spent the last 10 years working as a Senior AI Engineer, managing projects from the very first idea all the way to production. Her journey has taken her through very different worlds, from analyzing satellite images for big European utility companies to her current role as a researcher in medical imaging at Neurospin. Today, she works on brain images to help stroke patients recover. Sabrine 是一位应用数学工程师，过去 10 年一直担任高级 AI 工程师，负责管理从最初构思到最终投产的全过程。她的职业旅程跨越了截然不同的领域，从为欧洲大型公用事业公司分析卫星图像，到目前在 Neurospin 担任医学影像研究员。如今，她致力于研究脑部影像，以帮助中风患者康复。
Sabrine is also a mentor and the founder of Dataiilearn. She loves to write not only about code, but also about how to build a real career and how to make sure data science projects actually reach that final stage where they have a real impact. Sabrine 同时也是一位导师和 Dataiilearn 的创始人。她不仅热衷于编写关于代码的文章，还喜欢探讨如何建立真正的职业生涯，以及如何确保数据科学项目最终能产生实际影响。
A few months ago, you tackled an urgent question facing data professionals today: “is it still worth it?” Why did you decide to address it, and has your position evolved in the meantime? 几个月前，你探讨了当今数据专业人士面临的一个紧迫问题：“现在入行还值得吗？”你为什么决定讨论这个问题？你的立场在此期间有所改变吗？
Actually, my article “Data Science in 2026: Is It Still Worth It?” triggered an avalanche of messages on LinkedIn. I expected juniors to be worried about this question, but I was surprised to see that people with years of experience were also questioning the future. I have been in AI for 10 years now, and it’s true that in the beginning, just knowing Python and statistics/math made you a unicorn. Today, the market is saturated with new data scientists, and new tools based on AI agents are taking over the manual, simple tasks we used to do. 事实上，我的文章《2026 年的数据科学：还值得入行吗？》在 LinkedIn 上引发了大量讨论。我本以为只有初级从业者会担心这个问题，但令我惊讶的是，许多经验丰富的人也在质疑未来。我从事 AI 工作已经 10 年了，确实，在起步阶段，只要掌握 Python 和统计学/数学就能成为“独角兽”。而今天，市场充斥着新的数据科学家，基于 AI 智能体的新工具正在接管我们过去处理的那些简单、重复的手动任务。
So my position is still the same or maybe even stronger today: AI and data science are still worth it, but the “generalist data scientist” is a dying species. To survive, you must evolve beyond just models in a notebook. You need to master deployment, LLMs, RAG, and, most importantly, domain knowledge that helps data interpretability. If we build basic models in a notebook, of course our tasks could be done by agents. The jobs aren’t disappearing; they are just different. You need to build skills that adapt to this new market. 所以我的立场依然没变，甚至比以往更加坚定：AI 和数据科学依然值得投入，但“通才型数据科学家”正在成为濒危物种。为了生存，你必须超越仅仅在笔记本（Notebook）中构建模型的阶段。你需要掌握部署、大语言模型（LLM）、检索增强生成（RAG），以及最重要的一点——有助于数据可解释性的领域知识。如果我们只是在笔记本里构建基础模型，那么我们的任务当然会被 AI 智能体取代。工作并没有消失，只是形式变了。你需要培养能够适应这个新市场的技能。
You’ve written quite a lot about careers in data science and AI. How has your own journey shaped the insights you share with your readers? 你写过很多关于数据科学和 AI 职业发展的文章。你自己的职业历程是如何塑造你分享给读者的见解的？
From the beginning, my journey was never just about the code. I realized early on that solving real-world problems is something you don’t learn in a university or a bootcamp. You learn it by being in the trenches with real teams. In my years working with satellite images for energy and water companies, I learned that to create a real solution, you have to think “end-to-end.” If a model stays in a notebook, it has zero impact. This is why I write so much about MLOps — how to manage, deploy, and monitor models in production. 从一开始，我的职业旅程就不仅仅关乎代码。我很早就意识到，解决现实世界的问题是无法在大学或训练营中学到的。你必须深入一线，与真实的团队并肩作战才能学会。在为能源和水务公司处理卫星图像的那些年里，我学到要创造真正的解决方案，必须具备“端到端”的思维。如果模型只停留在笔记本里，它就没有任何影响力。这就是我为什么写这么多关于 MLOps 的文章——探讨如何管理、部署和监控生产环境中的模型。
Moving into the medical area added a new layer to my thinking. In the utility sector, if you make a mistake, you handle financial loss. But in medical imaging, you handle human lives. This shift taught me that AI can generate code, but it cannot understand the weight of a human decision. This is exactly why I’ve started to write about things like RAG, LLMs, and their impact. It’s not just a trendy topic for me; it’s about how difficult it is to make these tools reliable enough for a human to trust them 100%. 进入医疗领域为我的思考增添了新的维度。在公用事业领域，如果犯错，处理的是经济损失；但在医学影像领域，处理的是人的生命。这种转变让我明白，AI 可以生成代码，但它无法理解人类决策的分量。这正是为什么我开始撰写关于 RAG、LLM 及其影响的文章。对我来说，这不仅仅是一个热门话题，更关乎如何让这些工具变得足够可靠，以至于人类可以 100% 信任它们。
My insights come from this bridge: I have the industrial background of building for production, but I also have the research background where the methodology must be perfect. I write to share these technical skills, but also to help people navigate their own journeys. I want to show them the possibilities they have in this field, how to manage their path, and how to handle complex projects. I want my readers to see that a career in data is not always a straight line, and that’s okay. 我的见解源于这种跨界：我既有为生产环境构建系统的工业背景，也有要求方法论必须严谨的研究背景。我写作不仅是为了分享技术技能，也是为了帮助人们规划自己的职业旅程。我想向他们展示这个领域蕴含的可能性，如何管理自己的职业道路，以及如何处理复杂的项目。我希望读者明白，数据职业生涯并非总是一条直线，这没关系。
What are the most noticeable differences you observe between starting out now compared to your own early years in the field? How different is the playbook for early-career practitioners these days? 与你刚入行时相比，你观察到现在的起步阶段有哪些最显著的区别？如今初级从业者的“行动指南”有何不同？
The game has been totally rewritten. When I started, we were builders, and we spent weeks just cleaning data and setting up servers. Today, you have to be an AI Orchestrator. You can build a system in days that used to take months. I wouldn’t say it’s more difficult now, but it is definitely difficult if you try to start a career using the trendy skills from 10 years ago. Juniors today have so many options to get ready for the market. We have a goldmine of information on YouTube and on blogs. The real challenge now is filtering out the garbage. 游戏规则已经被彻底改写了。我刚开始时，我们是“构建者”，光是清洗数据和配置服务器就要花上几周时间。而今天，你必须成为一名“AI 编排者”。过去需要几个月才能完成的系统，现在几天就能建成。我不会说现在更难了，但如果你试图用 10 年前的热门技能来开启职业生涯，那确实会很困难。现在的初级从业者有太多渠道为市场做准备，YouTube 和博客上到处都是信息金矿。现在的真正挑战在于如何过滤掉垃圾信息。
The ones who survive are those who monitor and understand the market to adapt quickly. Of course, you need to understand the theoretical side of AI, but the real skill today is flexibility. It is not a good idea to only want to be an expert in one specific tool. 10 years ago, we were talking about switching from R to Python or from statistics to deep learning. Today, we are talking about switching to generative AI and agents. The foundations stay the same, but you need the flexibility to understand a new trend quickly, implement it, and answer your stakeholder’s needs. Flexibility has always been the “secret” skill of a data scientist, whether 10 years ago or today. 能生存下来的人，是那些能够时刻关注并理解市场，从而快速调整自己的人。当然，你需要理解 AI 的理论基础，但当今真正的核心技能是灵活性。只盯着某一种特定工具成为专家并不是明智之举。10 年前，我们讨论的是从 R 转向 Python，或者从统计学转向深度学习；今天，我们讨论的是转向生成式 AI 和智能体。基础知识是不变的，但你需要具备灵活性，以便快速理解新趋势、将其落地并满足利益相关者的需求。无论是在 10 年前还是今天，灵活性始终是数据科学家的“秘密”技能。
Your articles usually balance high-level information with hands-on insights. What do you hope your audience gains from reading your work? 你的文章通常兼顾宏观信息与实操见解。你希望读者从你的作品中获得什么？
When I write, I always keep in mind that I am sharing experiences to help people build their own expertise. For example, when I write about MLOps, I try to bridge the gap between the big picture of production and the practical technical steps. 我在写作时始终铭记，我是在分享经验以帮助人们建立自己的专业知识。例如，当我撰写关于 MLOps 的文章时，我试图弥合生产环境的宏观蓝图与具体技术步骤之间的鸿沟。